Topic and Sentiment Analysis on OSNs: a Case Study of Advertising Strategies on Twitter (original) (raw)

Towards Successful Social Media Advertising: Predicting the Influence of Commercial Tweets

ArXiv, 2019

Businesses communicate using Twitter for a variety of reasons -- to raise awareness of their brands, to market new products, to respond to community comments, and to connect with their customers and potential customers in a targeted manner. For businesses to do this effectively, they need to understand which content and structural elements about a tweet make it influential, that is, widely liked, followed, and retweeted. This paper presents a systematic methodology for analyzing commercial tweets, and predicting the influence on their readers. Our model, which use a combination of decoration and meta features, outperforms the prediction ability of the baseline model as well as the tweet embedding model. Further, in order to demonstrate a practical use of this work, we show how an unsuccessful tweet may be engineered (for example, reworded) to increase its potential for success.

Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment

In this paper we report research results investigating microblogging as a form of electronic word-of-mouth for sharing consumer opinions concerning brands. We analyzed more than 150,000 microblog postings containing branding comments, sentiments, and opinions. We investigated the overall structure of these microblog postings, the types of expressions, and the movement in positive or negative sentiment. We compared automated methods of classifying sentiment in these microblogs with manual coding. Using a case study approach, we analyzed the range, frequency, timing, and content of tweets in a corporate account. Our research findings show that 19% of microblogs contain mention of a brand. Of the branding microblogs, nearly 20% contained some expression of brand sentiments. Of these, more than 50% were positive and 33% were critical of the company or product. Our comparison of automated and manual coding showed no significant differences between the two approaches. In analyzing microblogs for structure and composition, the linguistic structure of tweets approximate the linguistic patterns of natural language expressions. We find that microblogging is an online tool for customer word of mouth communications and discuss the implications for corporations using microblogging as part of their overall marketing strategy.

Machine Learning Based Twitter Sentiment Analysis and User Influence

International Journal on Recent and Innovation Trends in Computing and Communication

The use of social media platforms, such as Twitter, has grown exponentially over the years, and it has become a valuable source of information for various fields, including marketing, politics, and finance. Sentiment analysis is particularly relevant in social media analysis. Sentiment analysis involves the use of natural language processing (NLP) techniques to automatically determine the sentiment expressed in a given text, such as positive, negative, or neutral. In this research paper, we focus on Twitter sentiment analysis and identify the most influential users in a given topic. We propose a methodology based on machine learning techniques to perform sentiment analysis and identify the most influential users on Twitter based on popularity. Specifically, we utilize a combination of NLP techniques, sentiment lexicons, and machine learning algorithms to classify tweets as positive, negative, or neutral. We then employ popularity calculations for each user to identify the top 10 mo...

Sentiment Analysis for Promotional Campaigns

Sentiment Analysis is a way to know one's opinion on a particular topic. The study helps in determine whether the subject in analysis has a positive impact or a negative impact. A promotional campaign of a product or service helps to reach to audience and to make them aware of the product or service. Our study uses natural language processing to determine the sentiments in the content from social media post. The social media sites are inherent with information like users opinions on a specific product or activities. Also, it is useful for gathering an overview of users opinions on a large-scale on certain topics. The potential to extract perceptions from social sites are globally embraced by various organisations. It plays vital role in different professions and marketing. The objective of project is to provide most recent tweets that can help in examining users opinions which can be further used for predictions in marketing, campaigning , entertainment industries.

Analyzing Social Media Sentiment: Twitter as a Case Study

ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal

This study examines the problem of Twitter sentimental analysis, which categorizes Tweets as positive or negative. Many applications require analyzing public mood, including organizations attempting to determine the market response to their products, political election forecasting, and macroeconomic phenomena such as stock exchange forecasting. Twitter is a social networking microblogging and digital platform that allows users to update their status in a maximum of 140 characters. It is a rapidly expanding platform with over 200 million registered users, 100 million active users, and half of the people log on every day, tweeting out over 250 million tweets. Public opinion analysis is critical for applications, including firms looking to understand market responses to their products, predict political choices, and forecast socio-economic phenomena like bonds. Through the deep learning methodologies, a recurrent neural network with convolutional neural network models was constructed t...

Big Data Analysis in Commercial Social Networks: Analysis of Twitter Reviews for Marketing Decision Making

European Journal of Information Technologies and Computer Science, 2023

Content generated by users on commercial social networks about products and brands generates large volumes of data that can be transformed into relevant and useful recommendations for marketing decisions. Every day, consumers post their opinions online on social networks about products they have purchased and used, and companies are increasingly interested in tracking this information in real time for better decision making. The main problem is to extract key information from consumers' textual comments and use it automatically to measure the quality of products or brands. In this work, we propose a hybrid approach to automatically analyze these reviews, assigning a quantitative score to negative and positive user content. The analysis of online consumer sentiment has increased significantly in recent years, being crucial to determine the success of businesses in a wide range of sectors, tourism, hospitality and e-commerce. In the same context, this work proposes a framework for analyzing the sentiment of reviews posted on the Twitter network towards products and brands. The first step is the construction of a dataset by collecting a set of reviews posted online on Twitter, processing and cleaning the textual data for better accuracy, and then developing a hybrid approach for product evaluation and polarities creation using lexicon-based methods and machine learningbased analysis techniques.

Measuring the Influence and Intensity of Customer’s Sentiments in Facebook and Twitter

GSTF Journal of Psychology, 2014

Organisations these days are actively using social media platforms to engage with potential and existing customers and monitor what they say about the organisation's product or service. The most important area within social media monitoring lies in how to gain insight for sentiment analysis. Sentiment analysis helps in effective evaluation of customer's sentiments in real time and takes on a special meaning in the context of online social networks like Twitter and Facebook, which collectively represent the largest online forum available for public opinion. Sentiment Analysis is not about retrieving and analyzing the analytics purely on the basis of positive, negative or neutral sentiment. It is imperative to assess the influencers of the sentiments in terms of Retweet and Share option used by them on Twitter and Facebook platform respectively. Measuring the intensity is other important aspect of sentiment analysis process. What kind of nouns, adjectives, verbs and adverbs are used in the opinion across the Twitter and Facebook platform matters as well since it exhibits the intensity of the underlying emotion in the text written. This study was conducted to propose a framework to identify and analyse the positive and negative sentiments present in Twitter and Facebook platforms and an algorithm was prepared to measure the intensity and influence of the positive, negative sentiment in particular using the document and sentence level analysis technique.